from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-09 14:07:50.840529
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 09, Dec, 2020
Time: 14:07:55
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.4581
Nobs: 135.000 HQIC: -44.6079
Log likelihood: 1430.15 FPE: 1.93325e-20
AIC: -45.3949 Det(Omega_mle): 1.01620e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.492289 0.178180 2.763 0.006
L1.Burgenland 0.142184 0.085669 1.660 0.097
L1.Kärnten -0.299097 0.072239 -4.140 0.000
L1.Niederösterreich 0.113852 0.205438 0.554 0.579
L1.Oberösterreich 0.300381 0.170905 1.758 0.079
L1.Salzburg 0.158744 0.086920 1.826 0.068
L1.Steiermark 0.085753 0.122604 0.699 0.484
L1.Tirol 0.164211 0.081229 2.022 0.043
L1.Vorarlberg 0.000515 0.078586 0.007 0.995
L1.Wien -0.136825 0.163347 -0.838 0.402
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.532034 0.227281 2.341 0.019
L1.Burgenland 0.001611 0.109276 0.015 0.988
L1.Kärnten 0.334371 0.092146 3.629 0.000
L1.Niederösterreich 0.118739 0.262050 0.453 0.650
L1.Oberösterreich -0.189647 0.218001 -0.870 0.384
L1.Salzburg 0.196073 0.110873 1.768 0.077
L1.Steiermark 0.223459 0.156389 1.429 0.153
L1.Tirol 0.149488 0.103614 1.443 0.149
L1.Vorarlberg 0.202206 0.100242 2.017 0.044
L1.Wien -0.549432 0.208360 -2.637 0.008
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.314849 0.077742 4.050 0.000
L1.Burgenland 0.105581 0.037379 2.825 0.005
L1.Kärnten -0.021875 0.031519 -0.694 0.488
L1.Niederösterreich 0.124402 0.089635 1.388 0.165
L1.Oberösterreich 0.281166 0.074568 3.771 0.000
L1.Salzburg -0.011731 0.037925 -0.309 0.757
L1.Steiermark -0.045974 0.053494 -0.859 0.390
L1.Tirol 0.092951 0.035442 2.623 0.009
L1.Vorarlberg 0.128619 0.034288 3.751 0.000
L1.Wien 0.039971 0.071271 0.561 0.575
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.174514 0.090133 1.936 0.053
L1.Burgenland 0.002891 0.043336 0.067 0.947
L1.Kärnten 0.033123 0.036542 0.906 0.365
L1.Niederösterreich 0.055997 0.103921 0.539 0.590
L1.Oberösterreich 0.375906 0.086452 4.348 0.000
L1.Salzburg 0.089566 0.043969 2.037 0.042
L1.Steiermark 0.205948 0.062019 3.321 0.001
L1.Tirol 0.033785 0.041090 0.822 0.411
L1.Vorarlberg 0.108407 0.039753 2.727 0.006
L1.Wien -0.083009 0.082629 -1.005 0.315
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.644462 0.193868 3.324 0.001
L1.Burgenland 0.071248 0.093212 0.764 0.445
L1.Kärnten -0.011231 0.078599 -0.143 0.886
L1.Niederösterreich -0.083435 0.223525 -0.373 0.709
L1.Oberösterreich 0.114976 0.185952 0.618 0.536
L1.Salzburg 0.041799 0.094573 0.442 0.659
L1.Steiermark 0.119398 0.133398 0.895 0.371
L1.Tirol 0.235154 0.088381 2.661 0.008
L1.Vorarlberg 0.028559 0.085505 0.334 0.738
L1.Wien -0.141661 0.177729 -0.797 0.425
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.219069 0.133253 1.644 0.100
L1.Burgenland -0.050179 0.064068 -0.783 0.434
L1.Kärnten -0.017893 0.054024 -0.331 0.740
L1.Niederösterreich 0.174229 0.153638 1.134 0.257
L1.Oberösterreich 0.397973 0.127812 3.114 0.002
L1.Salzburg -0.036107 0.065004 -0.555 0.579
L1.Steiermark -0.050690 0.091690 -0.553 0.580
L1.Tirol 0.200068 0.060748 3.293 0.001
L1.Vorarlberg 0.034040 0.058771 0.579 0.562
L1.Wien 0.141681 0.122160 1.160 0.246
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.222549 0.169920 1.310 0.190
L1.Burgenland 0.071129 0.081697 0.871 0.384
L1.Kärnten -0.073052 0.068890 -1.060 0.289
L1.Niederösterreich -0.072522 0.195914 -0.370 0.711
L1.Oberösterreich -0.090976 0.162982 -0.558 0.577
L1.Salzburg 0.009366 0.082891 0.113 0.910
L1.Steiermark 0.384740 0.116920 3.291 0.001
L1.Tirol 0.527662 0.077464 6.812 0.000
L1.Vorarlberg 0.224948 0.074943 3.002 0.003
L1.Wien -0.199490 0.155774 -1.281 0.200
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.094023 0.196387 0.479 0.632
L1.Burgenland 0.035693 0.094423 0.378 0.705
L1.Kärnten -0.082190 0.079620 -1.032 0.302
L1.Niederösterreich 0.176016 0.226430 0.777 0.437
L1.Oberösterreich 0.032720 0.188369 0.174 0.862
L1.Salzburg 0.218387 0.095802 2.280 0.023
L1.Steiermark 0.177493 0.135131 1.313 0.189
L1.Tirol 0.064192 0.089530 0.717 0.473
L1.Vorarlberg 0.029870 0.086616 0.345 0.730
L1.Wien 0.264977 0.180038 1.472 0.141
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.594112 0.109004 5.450 0.000
L1.Burgenland -0.011514 0.052409 -0.220 0.826
L1.Kärnten -0.000853 0.044193 -0.019 0.985
L1.Niederösterreich -0.041009 0.125679 -0.326 0.744
L1.Oberösterreich 0.289605 0.104553 2.770 0.006
L1.Salzburg 0.007051 0.053175 0.133 0.895
L1.Steiermark 0.016000 0.075004 0.213 0.831
L1.Tirol 0.073593 0.049693 1.481 0.139
L1.Vorarlberg 0.175508 0.048076 3.651 0.000
L1.Wien -0.095346 0.099930 -0.954 0.340
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.108484 -0.025417 0.190883 0.248833 0.025882 0.079115 -0.134259 0.139397
Kärnten 0.108484 1.000000 -0.046406 0.184981 0.108640 -0.153575 0.185231 0.014169 0.272207
Niederösterreich -0.025417 -0.046406 1.000000 0.250182 0.067334 0.186991 0.087182 0.034793 0.370836
Oberösterreich 0.190883 0.184981 0.250182 1.000000 0.258846 0.272143 0.079112 0.059054 0.061004
Salzburg 0.248833 0.108640 0.067334 0.258846 1.000000 0.137676 0.046046 0.083918 -0.042059
Steiermark 0.025882 -0.153575 0.186991 0.272143 0.137676 1.000000 0.083228 0.070978 -0.165710
Tirol 0.079115 0.185231 0.087182 0.079112 0.046046 0.083228 1.000000 0.131173 0.109488
Vorarlberg -0.134259 0.014169 0.034793 0.059054 0.083918 0.070978 0.131173 1.000000 0.064225
Wien 0.139397 0.272207 0.370836 0.061004 -0.042059 -0.165710 0.109488 0.064225 1.000000